Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/datacore-one/datacore/context-maintainergit clone --depth 1 https://github.com/datacore-one/datacoreWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00101 | $0.02450 |
| Opus 5 | $0.00051 | $0.01225 |
| Sonnet 5 | $0.00020 | $0.00490 |
| Haiku 4.5 | $0.00010 | $0.00245 |
Grade A, and why
context-maintainer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Maintainer Agent
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:context-maintainer - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/context-maintainer.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
When to Reference DIP-0002
Always reference when:
- Validating layer separation (public/org/team/local)
- Rebuilding composed CLAUDE.md files
- Checking private content leakage
- Syncing agent/command tables with actual files
Key decisions this DIP informs:
- Content classification by privacy level
- Layer composition order and precedence
- Private content detection patterns
- CLAUDE.md structure optimization
Quick Reference
| Question | Answer |
|---|---|
| Where is CLAUDE.md? | Root of each space + ~/Data/ |
| Where are layers? | CLAUDE.base.md, .space.md, .local.md |
| What triggers full sync? | Weekly review, manual request |
| What triggers quick sync? | Daily end workflow |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
scaffolding-auditor |
May call me for context validation |
session-learning |
Updates may trigger context rebuild |
Integration Points
- DIP-0002 - Follows layered context pattern
- context_merge.py - Uses for composition and validation
Maintains CLAUDE.md and other layered context files across the Datacore system.
Purpose
- Validation: Ensure context files follow the layered pattern (DIP-0002)
- Synchronization: Keep CLAUDE.md in sync with actual system state (agents, commands, modules)
- Optimization: Apply CLAUDE.md optimization patterns for effective AI context
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 325 lines · 101 tokens per session scan A 857c988c66c1
context-maintainer is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed 2d ago), licensed MIT. It adds 101 tokens to every session and 2,450 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
01-crm-pull
Fetch contacts, actions, pipeline data from CRM (Notion or local markdown).
01-calendar-pull
Fetch calendar events for the next 7 days via Google Calendar MCP.
verification-gate
Evidence-before-claims gate. Use before declaring work complete, fixed, or passing — before committing or creating PRs. Requires running verification commands, driving the affected flow end-to-end to observe real behaviour, and confirming output before any success claims. Adapted from Superpowers'…
keystone
Structured end-to-end trace to find the FIRST broken link in a specific claim's dependency chain. Single-claim depth probe — NOT a breadth reviewer. Use when a consequential claim ("X is enforced", "Y has a fallback", "Z reaches the main agent") needs primary-evidence verification across its full chain. Advisory…
brainstormer
Creative research and solution design agent. Takes a problem statement, surveys prior art (vault memory, web, papers), generates 3-5 ranked solution ideas with effort/impact/risk estimates, and identifies non-obvious connections. Use when stuck on a challenge, exploring design alternatives, or wanting creative input…
code-reviewer
Post-implementation, pre-commit review of actual code changes against Deus-specific rules stored in a versioned rules file. Runs on the working-tree + staged diff like a PR reviewer tuned to this repo's standards (CI gates, cross-platform, token efficiency, security basics, cleanup, type safety, comment discipline…